Pre-requisites

For this workshop, participants should be familiar with R at the level of the R Fundamentals Bootcamp, another introductory R workshop, or be a self-taught R coder.

Welcome

In this workshop, you will learn how to generate interactive plots that can host multi-dimensional data using plotly. We will be using the iris dataset, that contains information on petal and sepal dimensions for flowers from 3 different iris species.

Installation

First,we will install and load all required packages.

#Install required packages

if (!require(ggplot2)) install.packages(ggplot2)
if (!require(plotly)) install.packages("plotly")
if (!require(MASS)) install.packages("MASS")

#Load packages
library(datasets)
library(ggplot2)
library(plotly)
library(MASS)

Loading the iris dataset

We will start by loading the iris data and summarize the data to understand the different variables and structure it has.

#Load data 
data(iris)

summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

The iris dataset has 5 columns: sepal length, sepal width, petal length, petal width and the species of the iris flower.

Creating 2D visualizations

Next, we will create a basic 2D static visualization using ggplot2. We will generate a scatter plot to gauge the relationship between 2 variables - petal length and sepal length.

#Plot sepal length v/s petal length 
gg_fig <- ggplot(iris) + 
  geom_point(aes(x = Petal.Length, y = Sepal.Length))

gg_fig

Exercise 1

Create a 2D scatter plot comparing petal length and sepal length with points colored based on iris species.

Creating an interactive plot using plotly

plotly is an R package used to create interactive visualizations. It can be used to make existing ggplot2 objects interactive or to create plots from scratch. Interactive plotting allows you to zoom, hover, filter, and click on plot elements without needing to re-plot the figure.

We will start by creating an interactive visualization of the existing figure,gg_fig, for sepal length v/s petal length. This can be done easily using the ggplotly function

ggplotly(gg_fig)

Alternatively, the plot_ly() function can be used to create the interactive plot directly from data. The type argument is used to specify what type of chart (scatter , box or bar). Similar to the concept of layers in ggplot, in plotly each visual element is called a trace. For instance, multiple groups of data (such as petal and sepal length for different iris species) can be represented as multiple traces. We can manually add traces to an existing plot using add_trace() function. Unlike ggplot, the data variables need to be passed as formulas by using ~ before the variable name.

We will now generate a figure, where we will add the versicolor species of iris flowers as a trace to the figure.

##Adding traces manually

#Initialize the plot
fig <- plot_ly()

#Subset data for versicolor species  and add the data as a trace
df_subset <- subset(iris, Species == 'versicolor')
fig <- fig %>%
  add_trace(data = df_subset,
              x = ~Petal.Length,
              y = ~Sepal.Length,
              type = 'scatter',
              mode = 'markers',
              name = 'versicolor')
  
fig

Next, we will add the other two species as new traces to the figure.

#Subset data for setosa species  and add the data as a trace
df_subset <- subset(iris, Species == 'setosa')
fig <- fig %>%
  add_trace(data = df_subset,
              x = ~Petal.Length,
              y = ~Sepal.Length,
              type = 'scatter',
              mode = 'markers',
              name = 'setosa')
#Visualize
fig
#Subset data for virginica species  and add the data as a trace

df_subset <- subset(iris, Species == 'virginica')
fig <- fig %>%
  add_trace(data = df_subset,
              x = ~Petal.Length,
              y = ~Sepal.Length,
              type = 'scatter',
              mode = 'markers',
              name = 'virginica')
  
fig

Exercise 2

Create a boxplot using plot_ly() to measure the sepal length distribution for each species, with each species manually added as a new trace.

For scatter plots, the mode argument specifies how data points should appear in the scatter plot. Options include "markers" (points), "line" and "text". Combinations of multiple modes can be used by adding a “+” between them. For instance "markers+line" will represent observations as points with lines .

To our existing figure fig, we will add a trace where each point has a text label telling us the petal width for that observation.

fig %>%
    add_trace(data = iris,
              x = ~Petal.Length,
              y = ~Sepal.Length,
              type = 'scatter',
              mode ='text',
              text = ~Petal.Width)

Alternatively, we can display text on hover to have a cleaner plot. In this case, we simply specify the label to the text argument while plotting the scatter plot. Additionally, the plot_ly() function can also be used by itself (without adding new traces) to generate figures. Here is an example where we are not adding a new trace containing the text, but rather including it in the original plotting function itself.

fig <- plot_ly(data = iris,
               x = ~Petal.Length,
               y = ~Sepal.Length,
               text = ~paste("Petal Width: ", Petal.Width, '<br>Sepal Width:', Sepal.Width),
               type = 'scatter',
               mode = 'markers',
               color = ~Species)
fig

The layout function allows us to control the appearance of the plot. This function can be used to change the appearance and location of the title, axes, legend and other elements in the plot.

fig <- fig %>% 
  layout(title = "Iris: Petal Length v/s Sepal Length",
         xaxis = list(title = "Petal Length (cm)"), 
         yaxis = list(title = "Sepal Length (cm)"))
fig

Exercise 3

Change the layout of the boxplot generated in Exercise 2 to add a title and relabel the x and y-axes.

Creating 3D plots using plotly

To create a 3D plot with an additional variable, we set the the type argument in the plot_ly() function to "scatter3d". Additionally we specify the third variable as the z-axis for the plot. Below is an example of a 3D plot with the petal length, petal width and sepal length along the 3 axes.

fig_3d <- plot_ly(data = iris,
                   x = ~Petal.Length,
                   y = ~Sepal.Length,
                   z = ~Petal.Width,
                   type = 'scatter3d',
                   mode = 'markers',
                   color = ~Species) 
fig_3d  <- fig_3d %>% 
  layout(scene = list(xaxis = list(title = "Petal Length (cm)"),
                      yaxis = list(title = "Sepal Length (cm)"),
                      zaxis = list(title = "Petal Width (cm)")))

Such 3D plots can also be used to create surface plots where the z-axis represents the density of points. Here, we use the kde2d() function from MASS to get the joint 2D density of points based on the petal length and sepal length. We set the type to "surface" to generate a surface plot.

#Extract 2D density  numeric matrix
density <- kde2d(iris$Petal.Length, iris$Sepal.Length, n = 100)

surf_3d <- plot_ly(x = ~density$x,
                   y = ~density$y,
                   z = ~density$z, 
                   type = 'surface') 

surf_3d  <- surf_3d %>% 
  layout(title = "Iris: Petal Length v/s Sepal Length density",
         scene = list(xaxis = list(title = "Petal Length (cm)"),
                      yaxis = list(title = "Sepal Length (cm)"),
                      zaxis = list(title = "Density")))
surf_3d

Adding sliders to interactive plots

One of the major advantages of using interactive plotting is the ability to animate plots. Sliders allow the user to selectively view or filter data in the plot. A slider or animation can have multiple frames, with each frame representing a different filter for the data. Each frame can represent a set of active traces that can be visualized in the plot. By changing which frame is being visualized using the slider, the viewer can decide which set of traces are actively being visualized. When the frame is shifted on the slider, traces associated with the previous frame are hidden and those for the current frame are visualized.

Now, we will generate a figure with a slider that allows the viewer to filter the data based on species. First, we create the steps variable that represents which set of traces are visible in each frame. steps is a list of lists where the first list represents each frame and the lists within it contain information about active traces in that frame.

#Initialize the list of steps, this list has a length equal to the number of frames
steps <- list()

#Only the first trace (out of three traces) is visible when the frame is set to setosa
steps[[1]] <- list(args = list('visible', 
                               c(TRUE,FALSE,FALSE)),
                   method = 'restyle',
                   label = 'setosa')

#Only the second trace is visible when the frame is set to versicolor
steps[[2]] <- list(args = list('visible', 
                               c(FALSE,TRUE,FALSE)),
                   method ='restyle',
                   label = 'versicolor')


#Only the third trace is visible when the frame is set to virginica
steps[[3]] <- list(args = list('visible', 
                               c(FALSE,FALSE,TRUE)),
                   method = 'restyle',
                   label = 'virginica')

Next, we add the three traces corresponding to the setosa, versicolor and virginica species.

#Initialize plot
fig <- plot_ly()

#Set the active trace for when the plot is first plotted. This represents the initial frame for the plot.
#Here, we are setting the first species as active. 
active_trace <- c(TRUE,FALSE,FALSE)

#We add data for each species as a trace
for (sp in unique(iris$Species)) {
  
  #Get index of the species
  idx  <- which(unique(iris$Species) == sp)
  
  #Subset species specific data
  df_subset <- subset(iris, Species == sp)
  
  #Add data for each species as a new trace
  fig <- fig %>%
    add_trace(data = df_subset,
              x = ~Petal.Length,
              y = ~Sepal.Length,
              type = 'scatter',
              mode = 'markers',
              name = sp,
              visible = active_trace[idx], #This makes sure that in the initial plot only the species for the first frame is visible.
              showlegend = FALSE)
}

The appearance of the slider is customized using layout.

#Slider is added to the plot using the layout function
fig <- fig %>%
  layout(sliders = list(list(active = 0, #Specifies which frame is active when first plotted (where is the slider positioned)
                             currentvalue = list(prefix = "Species: "), #Label for the slider
                             steps = steps)),#The list of active traces for each frame
         yaxis=list(range=c(4,8)), #Set y axis limits
         xaxis=list(range=c(1,7))) #Set x axis limits

fig

Exercise 4

Create a slider that plots petal width v/s sepal width for samples with petal length above and below the median within their species.

Solutions

Exercise 1

Create a 2D scatter plot comparing petal length and sepal length with each point colored based on the iris species it belongs to

#Plot sepal length v/s petal length with each point colored based on Species
p <- ggplot(iris) + 
  geom_point(aes(x = Petal.Length, y= Sepal.Length, color= Species))
p

Exercise 2

Create a boxplot using plotly measuring the sepal length distribution for each species, with each species added as a new trace.

##Adding traces manually

#Initialize the plot
fig <- plot_ly()

#Use a for loop to subset data for each species  and add the data as a trace
for (sp in unique(iris$Species)) {
  df_subset <- subset(iris, Species == sp)
  
  fig <- fig %>%
    add_trace(data = df_subset,
              y = ~Sepal.Length,
              type = 'box',
              name = sp)
  
}

fig

Exercise 3

Change the layout of the boxplot generated in Exercise 2 to add a title and relabel the x and y-axes.

fig <- fig %>% layout(title = "Iris: Sepal Lengths across species ",
                      xaxis = list(title = "Species"),
                      yaxis = list(title = "Sepal Length (cm)"))
fig

Exercise 4

Create a slider that plots petal width v/s sepal width for samples with petal length above and below the median within their species.

#Classify samples as having petal length above or below the median within their species
iris_classified <- iris %>% 
  group_by(Species) %>%  #Group by each species
  mutate(petal_length_class = Petal.Length > median(Petal.Length)) #Add a column specifying if petal length is greater than median or not


#Initialize the list of steps, this list has a length equal to the number of frames
steps <- list()

#Set the active trace for when the plot is first plotted. This represents the initial frame for the plot.
#Here, we are setting the first group as active. 
active_trace = c(TRUE,FALSE)
labels <- c('Above Median','Below Median')


#Initialize plot with the first frame (above median)
fig <- plot_ly(data = iris_classified[iris_classified$petal_length_class,],
              x = ~Petal.Width,
              y = ~Sepal.Width,
              color= ~Species,
              type = 'scatter',
              mode = 'markers',
              visible = TRUE,
              showlegend = FALSE)

#Adding the second frame (below median)
fig <- fig %>%
    add_trace(data = iris_classified[!iris_classified$petal_length_class,],
              x = ~Petal.Width,
              y = ~Sepal.Width,
              color= ~Species,
              type = 'scatter',
              mode = 'markers',
              visible = FALSE,
              showlegend = FALSE)

#Creating the slider frames
steps<-list(list(args= list('visible', active_trace),method='restyle',label=labels[1]),
            list(args= list('visible', c(FALSE,TRUE)),method='restyle',label=labels[2]))
  
  
#Slider is added to the plot using the layout function
fig <- fig %>%layout(sliders = list(list(active = 0, #Specifies which frame is active when first plotted (where is the slider positioned)
                             currentvalue = list(prefix = "Value: "), #Label for the slider
                             steps = steps)),
                     yaxis=list(range=c(2,4.5)), 
                     xaxis=list(range=c(0,3))
                     ) 

fig